Changes in Liver Fibrosis as Determined by FIB-4 Score Following Sofosbuvir-Based Treatment Regimes Without Interferon

Objective: To determine the mean change in liver fibrosis as evaluated using the FIB-4 score following Sofosbuvir based treatment regimens without interferon. Methodology: This prospective observational study was conducted at the Department of Medicine, Federal Government Services Hospital, Islamabad, from January 09, 2019 to January 03, 2020. A total of seventy (n=70) patients of either gender between age 18-75 years who were diagnosed with cases of HCV infection were enrolled in this study. All patients were treated with Sofosbuvir-based treatment regimens and were assessed for liver fibrosis using the FIB-4 score at baseline, at end of treatment (EOT) and 12 weeks after EOT. Results: The mean FIB-4 score at baseline was 2.45±0.42, at EOT was 1.0981±0.33 and at 12 weeks after EOT was 1.51±0.32.  As compared to the baseline, the mean FIB-4 score was significantly lesser at EOT (P=0.001) and at 12 weeks after EOT (P=0.001). A similar trend was observed across all stratified groups, i.e., age, gender, and type of patients (P<0.05 across all groups). Conclusion: The sofosbuvir-based treatment regimen significantly reduced liver fibrosis at EOT and 12 weeks after EOT, as evidenced by FIB-4 scores that were significantly lower than baseline at EOT and 12 weeks after EOT

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Biomarkers and Coronary Microvascular Dysfunction in Women With Angina and No Obstructive Coronary Artery Disease

Background Coronary microvascular dysfunction (CMD) is a major cause of ischemia with no obstructed coronary arteries. Objectives The authors sought to assess protein biomarker signature for CMD. Methods We quantified 184 unique cardiovascular proteins with proximity extension assay in 1,471 women with angina and no obstructive coronary artery disease characterized for CMD by coronary flow velocity reserve (CFVR) by transthoracic echo Doppler. We performed Pearson's correlations of CFVR and each of the 184 biomarkers, and principal component analyses and weighted correlation network analysis to identify clusters linked to CMD. For prediction of CMD (CFVR &lt; 2.25), we applied logistic regression and machine learning algorithms (least absolute shrinkage and selection operator, random forest, extreme gradient boosting, and adaptive boosting) in discovery and validation cohorts. Results Sixty-one biomarkers were correlated with CFVR with strongest correlations for renin (REN), growth differentiation factor 15, brain natriuretic protein (BNP), N-terminal-proBNP (NT-proBNP), and adrenomedullin (ADM) (all P &lt; 1e-06). Two principal components with highest loading on BNP/NTproBNP and interleukin 6, respectively, were strongly associated with low CFVR. Weighted correlation network analysis identified 2 clusters associated with low CFVR reflecting involvement of hypertension/vascular function and immune modulation. The best prediction model for CFVR &lt;2.25 using clinical data had area under the receiver operating characteristic curve (ROC-AUC) of 0.61 (95% CI: 0.56-0.66). ROC-AUC was 0.66 (95% CI: 0.62-0.71) with addition of biomarkers (P for model improvement = 0.01). Stringent two-layer cross-validated machine learning models had ROC-AUC ranging from 0.58 to 0.66; the most predictive biomarkers were REN, BNP, NT-proBNP, growth differentiation factor 15, and ADM. Conclusions CMD was associated with pathways particularly involving inflammation (interleukin 6), blood pressure (REN, ADM), and ventricular remodeling (BNP/NT-proBNP) independently of clinical risk factors. Model prediction improved with biomarkers, but prediction remained moderate.</p